{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "714d2dd4-e537-412a-a853-727629a1a5e4",
   "metadata": {},
   "source": [
    "**Pedro Rivero**  \n",
    "Quantum Developer *@ IBM Quantum*  \n",
    "pedro.rivero@ibm.com "
   ]
  },
  {
   "cell_type": "raw",
   "id": "a807d475-8d22-426d-8cdc-835c3561bc9d",
   "metadata": {},
   "source": [
    "# Make sure that Qiskit Aer is installed for noise models\n",
    "!pip install qiskit-aer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "708128eb-fc4a-486f-be48-f8b20bb3b9da",
   "metadata": {},
   "source": [
    "# Zero noise extrapolation (ZNE)\n",
    "Learn how to mitigate errors in your expectation value calculations using a technique known as _Zero noise extrapolation_ (ZNE). \n",
    "\n",
    "This software is built on top the the _Estimator_ primitive therefore, to follow along, basic familiriaty with said tool is requiered and will be assumed. See here for [introductory material](0-estimator.ipynb).\n",
    "\n",
    "\n",
    "## Overview of ZNE\n",
    "![Zero noise extrapolation (ZNE)](../media/zne.png)\n",
    "\n",
    "### Noise amplification\n",
    "The original implementation of ZNE carried out noise amplification at the hardware level (i.e. _analog_ noise amplification), by moduling the microwave pulses implementing the ciruit's unitary gates. This process is significantly involved and requires calibration of the device before each noisy experiment, which is why a more relaxed alternative was devised: gate folding (i.e. _digital_ noise amplification).\n",
    "![Noise amplification](../media/noise_amplification-folding.png)\n",
    "\n",
    "### Extrapolation\n",
    "This process was first carried out with a technique known as _Richardson extrapolation_. Nonetheless, we often use general regression methods to account for several different noise profiles:\n",
    "![Extrapolation](../media/noise_profile.png)\n",
    "\n",
    "\n",
    "## Demo\n",
    "This module works by injecting ZNE capabilities in any given implementation of the _Estimator_ primitive. Once this functionality is in place, it can be controlled through a `ZNEStrategy` object that encapsulates all necessary information for customizing the error mitigation process.\n",
    "\n",
    "### Building a ZNE Estimator\n",
    "Since we are going to be showcasing error mitigation, we need our calculations to be noisy. Therefore, we are going to be using `qiskit.primitives.BackendEstimator` alongside a fake noisy backend:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3c917874-a5e8-4a06-b4a6-956a081a2606",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.primitives import BackendEstimator\n",
    "from qiskit.providers.fake_provider import FakeNairobi  # We need noise!\n",
    "from zne import zne\n",
    "\n",
    "ZNEEstimator = zne(BackendEstimator)  # Any implementation of BaseEstimator is valid\n",
    "backend = FakeNairobi()\n",
    "estimator = ZNEEstimator(backend=backend)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "332007c5-1b72-4172-a913-135af95146d1",
   "metadata": {},
   "source": [
    "Notice how the `zne()` function, takes in any class implementing the `BaseEstimator` interface, and outputs a new, equivalent, class updated with ZNE capabilities. This new class is used in exactly the same way as its predecessor (i.e. can be configured in the same way), except that it will now accept on extra option describing the ZNE workflow that the user wants to accomplish: `zne_strategy`.\n",
    "\n",
    "Now that we have instantiated our estimator, lets put it to work:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "11778e57-037d-4e57-89f1-6b5ba6a4076f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1792.29x200.667 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  > Observable: ['ZZ']\n",
      "  > Expectation value: 0.921875\n",
      "  > Metadata: {'variance': 0.150146484375, 'shots': 1024}\n"
     ]
    }
   ],
   "source": [
    "from qiskit.circuit.random import random_circuit\n",
    "from qiskit.quantum_info import SparsePauliOp\n",
    "\n",
    "circuit = random_circuit(2, 4, seed=1).decompose(reps=1)\n",
    "observable = SparsePauliOp(\"ZZ\")\n",
    "\n",
    "job = estimator.run(circuit, observable)\n",
    "result = job.result()\n",
    "\n",
    "display(circuit.draw(\"mpl\"))\n",
    "print(f\"  > Observable: {observable.paulis}\")\n",
    "print(f\"  > Expectation value: {result.values[0]}\")\n",
    "print(f\"  > Metadata: {result.metadata[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79fc3924-673f-4766-b3e0-8610f7887e1f",
   "metadata": {},
   "source": [
    "And let's compare these results with an ideal simulation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "529b875d-6cc3-434f-bc7e-b199ff9200c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1792.29x200.667 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  > Observable: ['ZZ']\n",
      "  > Expectation value: 0.9999999999999984\n",
      "  > Metadata: {}\n"
     ]
    }
   ],
   "source": [
    "from qiskit.primitives import Estimator\n",
    "\n",
    "circuit = random_circuit(2, 4, seed=1).decompose(reps=1)\n",
    "observable = SparsePauliOp(\"ZZ\")\n",
    "\n",
    "job = Estimator().run(circuit, observable)  # !!!\n",
    "result = job.result()\n",
    "\n",
    "display(circuit.draw(\"mpl\"))\n",
    "print(f\"  > Observable: {observable.paulis}\")\n",
    "print(f\"  > Expectation value: {result.values[0]}\")\n",
    "print(f\"  > Metadata: {result.metadata[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f77085b0-0a48-4f87-a89d-fb27f4742cfe",
   "metadata": {},
   "source": [
    "Wait a second... the result with ZNE doesn't seem to be that good after all!\n",
    "\n",
    "That is because on the previous run, ZNE wasn't activated. Since no `zne_strtegy` option was provided, it defaulted to the standard behavior without error mitigation. This is a design feature, not a bug: in the sake of versatility.\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "    <i class=\"fas fa-info-circle\"></i>\n",
    "    <b>NOTICE</b><br/> \n",
    "    You can always change the default by passing the extra <code>zne_strategy</code> kwarg on instantiation, or editing the attribute with the same name:  \n",
    "    <pre><code>\n",
    "    estimator = ZNEEstimator(backend=backend, zne_strategy=zne_strategy)\n",
    "    estimator.zne_strategy = zne_strategy\n",
    "    </code></pre>\n",
    "</div>\n",
    "\n",
    "### Configuring ZNE\n",
    "\n",
    "#### Noise factors\n",
    "The easiest thing you can do to configure ZNE is providing a set of `noise_factors` to use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6cf2e305-83d5-478c-8798-f6ff5e016a29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1792.29x200.667 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  > Observable: ['ZZ']\n",
      "  > Expectation value: 0.9265950520833337\n",
      "  > Metadata: {'zne': {'noise_amplification': {'noise_amplifier': <CxAmplifier:{'noise_factor_relative_tolerance': 0.01, 'random_seed': None, 'sub_folding_option': 'from_first'}>, 'noise_factors': (1, 3, 5), 'values': (0.89453125, 0.822265625, 0.759765625), 'variance': (0.1998138427734375, 0.3238792419433594, 0.4227561950683594), 'shots': (1024, 1024, 1024)}, 'extrapolation': {'extrapolator': LinearExtrapolator}}}\n"
     ]
    }
   ],
   "source": [
    "from zne import ZNEStrategy\n",
    "\n",
    "circuit = random_circuit(2, 4, seed=1).decompose(reps=1)\n",
    "observable = SparsePauliOp(\"ZZ\")\n",
    "\n",
    "zne_strategy = ZNEStrategy(noise_factors=[1, 3, 5])\n",
    "job = estimator.run(circuit, observable, zne_strategy=zne_strategy)  # !!!\n",
    "result = job.result()\n",
    "\n",
    "display(circuit.draw(\"mpl\"))\n",
    "print(f\"  > Observable: {observable.paulis}\")\n",
    "print(f\"  > Expectation value: {result.values[0]}\")\n",
    "print(f\"  > Metadata: {result.metadata[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6d0dd10-f2a7-47e8-8dcc-dda536420abc",
   "metadata": {},
   "source": [
    "This time around we indeed got an improvement in the computed expectation value, which is now closer to the `exact = 1.0`.\n",
    "\n",
    "But the metadata looks very complex now! As a matter of fact, a lot of relevant information regarding the ZNE process has now been stored in an entry labeled `zne`. This entry is at the same time divided in two: `noise_amplification` and `extrapolation`, each of which holding intel on the corresponding task.\n",
    "\n",
    "Let's serialize it to jason and pretty print it to see what is going on more clearly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f0c0f61e-b2ac-47e0-aab0-41c1e9afe039",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"values\": [\n",
      "    0.9265950520833337\n",
      "  ],\n",
      "  \"metadata\": [\n",
      "    {\n",
      "      \"zne\": {\n",
      "        \"noise_amplification\": {\n",
      "          \"noise_amplifier\": \"<CxAmplifier:{'noise_factor_relative_tolerance': 0.01, 'random_seed': None, 'sub_folding_option': 'from_first'}>\",\n",
      "          \"noise_factors\": [\n",
      "            1,\n",
      "            3,\n",
      "            5\n",
      "          ],\n",
      "          \"values\": [\n",
      "            0.89453125,\n",
      "            0.822265625,\n",
      "            0.759765625\n",
      "          ],\n",
      "          \"variance\": [\n",
      "            0.1998138427734375,\n",
      "            0.3238792419433594,\n",
      "            0.4227561950683594\n",
      "          ],\n",
      "          \"shots\": [\n",
      "            1024,\n",
      "            1024,\n",
      "            1024\n",
      "          ]\n",
      "        },\n",
      "        \"extrapolation\": {\n",
      "          \"extrapolator\": \"LinearExtrapolator\"\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  ]\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "from zne.utils.serialization import EstimatorResultEncoder\n",
    "\n",
    "print(EstimatorResultEncoder.dumps(result, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44686c14-6478-4b26-ae2b-61b98c18468d",
   "metadata": {},
   "source": [
    "It is now clear that within `noise_amplification`, we can find all the information concerning the noisy experiements (e.g. noise factors, noisy values, estimated variances, shots). But there is one other entry: `noise_amplifier`.\n",
    "\n",
    "#### Noise amplifier\n",
    "As mentioned above, there are several different ways of amplifying the noise in your circuit, some of them being more involved than others. In this module we include two digital techniques: \n",
    "- `LocalFoldingAmplifier` \n",
    "- `GlobalFoldingAmplifier`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "20e333bf-0b8b-4532-8b28-eff39190abd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from zne.noise_amplification import LocalFoldingAmplifier, GlobalFoldingAmplifier\n",
    "\n",
    "global_amplifier = GlobalFoldingAmplifier()\n",
    "local_amplifier = LocalFoldingAmplifier(gates_to_fold=2)  # Fold two-qubit gates\n",
    "\n",
    "zne_strategy = ZNEStrategy(\n",
    "    noise_factors=range(1, 9), \n",
    "    noise_amplifier=local_amplifier,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33065bcb-7ab4-42d8-8c2d-dadb4c3048ab",
   "metadata": {},
   "source": [
    "If no `noise_amplifier` is provide, a default will be applied: `LocalFoldingAmplifier` folding all gates. See [this tutorial](2-noise_amplification.ipynb) for more information on how to configure and use these noise amplification strategies.\n",
    "\n",
    "#### Extrapolator\n",
    "In the metadata, we also see an entry similar to `noise_amplifier`, but under `extrapolation`: `extrapolator`. In this module we provide a basic `PolynomialExtrapolator` whose degree can be configured at will and default to `degree = 1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e34d5ed5-8f6e-4023-8f1e-a2556bbf5f39",
   "metadata": {},
   "outputs": [],
   "source": [
    "from zne.extrapolation import PolynomialExtrapolator\n",
    "\n",
    "extrapolator = PolynomialExtrapolator(degree=2)\n",
    "\n",
    "zne_strategy = ZNEStrategy(\n",
    "    noise_factors=[1.0, 1.5, 2.0], \n",
    "    extrapolator=extrapolator\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a41be9ec-8129-4714-8137-3d389fc4057d",
   "metadata": {},
   "source": [
    "#### Custom ZNE strategies\n",
    "Furthermore, the current implementation of `ZNEStrategy` allows us to easily build custom noise amplification, and extrapolation techniques:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "370601bf-edfe-496d-88b6-3205b2cde6ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "from zne.extrapolation import Extrapolator, ReckoningResult\n",
    "from zne.noise_amplification import CircuitNoiseAmplifier\n",
    "\n",
    "\n",
    "############################  NOISE AMPLIFIER  ############################\n",
    "class CustomAmplifier(CircuitNoiseAmplifier):\n",
    "    def amplify_circuit_noise(self, circuit, noise_factor):\n",
    "        return circuit.copy()  # Dummy, nonperforming\n",
    "\n",
    "\n",
    "############################  EXTRAPOLATOR  ############################\n",
    "class CustomExtrapolator(Extrapolator):\n",
    "    @property\n",
    "    def min_points(self):\n",
    "        return 2\n",
    "    \n",
    "    def _extrapolate_zero(self, x_data, y_data, sigma_x, sigma_y):\n",
    "        value = 1.0\n",
    "        std_error = 1.0\n",
    "        metadata = {\"meta\": \"data\"}\n",
    "        return ReckoningResult(value, std_error, metadata)  # Dummy, nonperforming"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b302559-17f6-4cb6-8c34-a12a2e23bf4e",
   "metadata": {},
   "source": [
    "And we see that it now runs by passing it through `zne_strategy`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "303bc24f-bf7d-47fc-8dcc-e0680228c81d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 621.739x200.667 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  > Observable: ['ZZ']\n",
      "  > Expectation value: 1.0\n",
      "  > Metadata: {'zne': {'noise_amplification': {'noise_amplifier': CustomAmplifier, 'noise_factors': (1, 3), 'values': (0.03125, 0.025390625), 'variance': (0.9990234375, 0.9993553161621094), 'shots': (1024, 1024)}, 'extrapolation': {'extrapolator': CustomExtrapolator, 'variance': 0.0, 'metadata': None}}}\n"
     ]
    }
   ],
   "source": [
    "circuit = random_circuit(2, 2, seed=0).decompose(reps=1)\n",
    "observable = SparsePauliOp(\"ZZ\")\n",
    "\n",
    "zne_strategy = ZNEStrategy(\n",
    "    noise_factors=(1, 3),\n",
    "    noise_amplifier=CustomAmplifier(),\n",
    "    extrapolator=CustomExtrapolator(),\n",
    ")\n",
    "\n",
    "job = estimator.run(circuit, observable, zne_strategy=zne_strategy)\n",
    "result = job.result()\n",
    "\n",
    "display(circuit.draw(\"mpl\"))\n",
    "print(f\"  > Observable: {observable.paulis}\")\n",
    "print(f\"  > Expectation value: {result.values[0]}\")\n",
    "print(f\"  > Metadata: {result.metadata[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68bdfa3f-90f7-4fec-9d40-af6e6c240cf4",
   "metadata": {},
   "source": [
    "Notice how the mitigated value is indeed `value = 1.0` and the variance (i.e. in `extrapolation`) is `variance = 0.0`, just as we defined in our dummy implementation.\n",
    "\n",
    "### Complete run\n",
    "To wrap up, let us go through a complete run and plot the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c891743a-8cc8-497e-b675-f6ec924b3ccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1792.29x200.667 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  > Observable: ['ZZ']\n",
      "  > Expectation value: 0.9619140625000004\n",
      "  > Metadata: {'zne': {'noise_amplification': {'noise_amplifier': <LocalFoldingAmplifier:{'gates_to_fold': 2, 'noise_factor_relative_tolerance': 0.01, 'random_seed': None, 'sub_folding_option': 'from_first'}>, 'noise_factors': (1, 2, 3, 4, 5, 6, 7, 8), 'values': (0.892578125, 0.87890625, 0.82421875, 0.80078125, 0.75, 0.701171875, 0.6953125, 0.70703125), 'variance': (0.20330429077148438, 0.2275238037109375, 0.3206634521484375, 0.3587493896484375, 0.4375, 0.5083580017089844, 0.51654052734375, 0.5001068115234375), 'shots': (1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024)}, 'extrapolation': {'extrapolator': <PolynomialExtrapolator:{'degree': 2}>}}}\n"
     ]
    }
   ],
   "source": [
    "from qiskit.circuit.random import random_circuit\n",
    "from qiskit.primitives import BackendEstimator\n",
    "from qiskit.providers.fake_provider import FakeNairobi\n",
    "from qiskit.quantum_info import SparsePauliOp\n",
    "from zne import zne, ZNEStrategy\n",
    "from zne.noise_amplification import LocalFoldingAmplifier\n",
    "from zne.extrapolation import PolynomialExtrapolator\n",
    "\n",
    "## Build ZNEEstimator\n",
    "ZNEEstimator = zne(BackendEstimator)\n",
    "backend = FakeNairobi()\n",
    "estimator = ZNEEstimator(backend=backend)\n",
    "\n",
    "## Build our input circuit and observable\n",
    "circuit = random_circuit(2, 4, seed=1).decompose(reps=1)\n",
    "observable = SparsePauliOp(\"ZZ\")\n",
    "\n",
    "## Build a ZNE strategy\n",
    "zne_strategy = ZNEStrategy(\n",
    "    noise_factors=range(1, 9),\n",
    "    noise_amplifier=LocalFoldingAmplifier(gates_to_fold=2),\n",
    "    extrapolator=PolynomialExtrapolator(degree=2),\n",
    ")\n",
    "\n",
    "## Run experiment\n",
    "job = estimator.run(circuit, observable, zne_strategy=zne_strategy)  # !!!\n",
    "result = job.result()\n",
    "\n",
    "## Display results\n",
    "display(circuit.draw(\"mpl\"))\n",
    "print(f\"  > Observable: {observable.paulis}\")\n",
    "print(f\"  > Expectation value: {result.values[0]}\")\n",
    "print(f\"  > Metadata: {result.metadata[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "72a87c85-8652-44a5-a993-1a3b9fd1d69c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1050 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mpl.rcParams[\"figure.dpi\"] = 300\n",
    "plt.rcParams.update({\"text.usetex\": True, \"font.family\": \"Computer Modern Roman\"})\n",
    "\n",
    "############################  DATA  ############################\n",
    "exact = 1  # From simulation above\n",
    "mitigated = result.values[0]\n",
    "noise_factors = result.metadata[0][\"zne\"][\"noise_amplification\"][\"noise_factors\"]\n",
    "noisy_values = result.metadata[0][\"zne\"][\"noise_amplification\"][\"values\"]\n",
    "\n",
    "############################  PLOT  ############################\n",
    "plt.rcParams[\"figure.figsize\"] = (5,3.5)\n",
    "plt.grid(which='major',axis='both')\n",
    "\n",
    "plt.plot([0, noise_factors[-1]], [exact, exact], \"--\", label=f\"Exact\", color=\"#000000\")\n",
    "plt.scatter(0, mitigated, label=f\"Mitigated\", marker=\"x\", color=\"#785ef0\")\n",
    "if noise_factors[0] == 1:\n",
    "    plt.scatter(\n",
    "        noise_factors[0], noisy_values[0], \n",
    "        label=f\"Unmitigated\", marker=\"s\", color=\"#dc267f\",\n",
    "    )\n",
    "plt.plot(\n",
    "    noise_factors, noisy_values, \n",
    "    label=f\"Amplified\", marker=\"o\", color=\"#dc267f\",\n",
    ")\n",
    "\n",
    "plt.title(\"Zero noise extrapolation\")\n",
    "plt.xlabel(\"Noise Factor ($n$)\")\n",
    "plt.ylabel(f\"Expectation Value ($\\langle ZZ \\\\rangle$)\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e2b9fa42-8449-4954-935d-aff517b1621d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.22.0</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.10.5</td></tr><tr><td>Python compiler</td><td>Clang 13.1.6 (clang-1316.0.21.2.5)</td></tr><tr><td>Python build</td><td>main, Jun 23 2022 17:15:25</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>8</td></tr><tr><td>Memory (Gb)</td><td>32.0</td></tr><tr><td colspan='2'>Sat Oct 15 16:46:43 2022 EDT</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2022.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import qiskit.tools.jupyter  # pylint: disable=unused-import,wrong-import-order\n",
    "\n",
    "%qiskit_version_table\n",
    "%qiskit_copyright"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "zne",
   "language": "python",
   "name": "zne"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
